Title: Predicting time to graduation at a large enrollment American university
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto’s Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA). more »« less
Project‐based learning has shown promise in improving learning outcomes for diverse students. However, studies on its impacts have largely focused on the perceptions of students and instructors or students' immediate performance. This study reports the impact of taking a project‐based introductory engineering course on students' subsequent academic success.
Purpose/Hypothesis
This quantitative study examines characteristics related to enrollment in the project‐based introductory engineering course and subsequent academic performance. We hypothesized that participation in the course would be associated with higher academic performance in subsequent engineering courses. In addition, we examined heterogeneity effects for students traditionally underrepresented in engineering education.
Design/Method
This study utilized data on students' demographics, academic preparation, course enrollment, and course performance from 1,318 engineering students from a large public university in Southern California. Logistic regression analysis with robust standard errors examined enrollment patterns. We applied propensity scores as inverse‐probability weights in multiple linear models to calculate the average treatment effect on the treated for participants from the project‐based introductory engineering course in five subsequent engineering courses. This analysis was conducted for all students and for selected student subgroups.
Results
Enrollment in the project‐based introductory engineering course was positively associated with students' performance in some subsequent engineering courses and did not adversely affect students traditionally underrepresented in engineering.
Conclusions
This study provides an example of a project‐based introductory engineering course that can support students' academic success in engineering. The benefits detected for some student populations (e.g., female) are encouraging for broadening engineering pathways.
Ameri, Sattar; Fard, Mahtab J.; Chinnam, Ratna B.; Reddy, Chandan K.(
, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management)
Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of "at-risk'' students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods.
A combination of strategies was implemented to reduce barriers to transfer from associate to baccalaureate programs, and baccalaureate degree completion. These strategies include creation of the STEM Transfer Collaborative (STC). an adaption of the CUNY Pathways articulation initiative. Components of the STC include articulation agreements, shared professional development to align pedagogy and curriculum, outreach and collaboration by both the sending and receiving college faculty to begin transfer preparation and support before transfer occurs, and regular updates to community college faculty on the success of their transfer students. The second strategy employed is Momentum to the Baccalaureate (MB), an adaption of the CUNY Accelerated Study in Associate Programs (ASAP). MB provides support for junior and senior-level transfer students who are either community colleges associate degree graduates (external transfer) or associate degree graduates who transferred to bachelor’s programs at the same comprehensive college they earned their associate degree at, which has a 2+2 degree structure (internal transfer). Components of MB include personalized mentoring, advisement, and monthly stipends to students who maintain full-time enrollment and good academic standing. Participating majors include computer engineering technology, computer systems technology, construction management and civil engineering technology, electrical engineering technology, and applied chemistry. Propensity matching was used to evaluate the effectiveness of these strategies.
Participating campuses are part of the City University of New York (CUNY), and include six community colleges (Borough of Manhattan Community College, Bronx Community College, Guttman Community College, Hostos Community College, Kingsborough Community College, and LaGuardia Community College), five of which are Hispanic Serving Institutions (HSIs), and New York City College of Technology (City Tech), also an HSI, which offers associate and bachelor’s programs (2+2 structure).
Our first cohort of 40 students started upper-level studies in fall 2019, and has completed 2 years (four semesters) of post-associate degree study. The second cohort of 40 students, started in fall 2020, and has completed one year (two semesters) of post-associate degree study.
Cohort 1 students receiving MB, supports had a significantly higher graduation rate after two years than the college average. Additionally, for cohort 1, the STC seems to have reduced “transfer shock,” the typical drop in GPA the first semester after transfer. There was no statistical difference in GPA, credits completed and semester-to-semester persistence of internal and external transfers in the MB program. Cohort 1 external transfer students who received support though MB also had a statistically significant improvement in their semester GPAs for their first 3 semesters at City Tech compared to matched students who were not provided support in the junior and senior years. There was no statistically significant difference by the 4th semester.
Cohort 2 internal transfers receiving MB supports in their junior year had a statistically significant improvement in credits earned and persistence compared to a matched cohort without MB supports. There was no statistically significant improvement of external transfers compared to a matched cohort, who did not receive MB supports The inability of external transfer students to come to campus due to the pandemic, may have negated the sense of community and belonging that MB was intended to create.
Overall, these preliminary results suggest that targeted pre-transfer and post-transfer supports improve transfer student outcomes.
This project (NSF grant #1832457) was funded through the NSF Division of Education, Improving Undergraduate STEM Education: Hispanic-Serving Institutions Program.
According to the National Science Foundation, 50% of Black engineering students who have received a bachelor’s and master’s degree attended a community college at some point during their academic career. However, while research highlights the importance of supporting underrepresented racial and ethnic minorities (URMs) in STEM disciplines, there is a dearth of literature focusing on URMs in community colleges who pursue engineering and other science/math-based majors. Further, Black undergraduates in community colleges are often homogenized by area of study, with little regard for their specific major/discipline. Similarly, while engineering education research has begun to focus on the population of community college students, less attention has been paid to unpacking the experiences of racial subgroups of community college attendees. The engineering student transfer process has specific aspects related to it being a selective and challenging discipline (e.g., limited enrollment policies, engineering culture shock) that warrants a closer investigation.
The purpose of this paper is to examine the experiences of a small population of students who have recently transferred from several community colleges to one four-year engineering school. Specifically, we will present preliminary findings derived from interviews with three Black students who started their academic careers at several community colleges in a Mid-Atlantic state, before transferring to the flagship institution of that same state. Interview transcripts will undergo a thorough analysis and will be coded to document rich themes. Multiple analyses of coded interview data will be performed by several members of the research team, as well as external evaluation members who are leading scholars in STEM and/or transfer education research.
This research is part of a larger-scale, three year qualitative study, which will examine the academic trajectories of two distinct groups of Blacks in engineering majors: 1) Blacks born and educated in the United States and 2) Those born and educated in other countries. By looking at these populations distinctly, we will build upon past literature that disaggregates the experiences of Black STEM students who represent multiple identities across the African diaspora. Through this lens, we hope to highlight the impact that cultural background may have on the transfer experience.
The theoretical framework guiding this study posits that the persistence of Black transfer students in engineering is a longitudinal process influenced by the intersection of both individual and institutional factors. We draw from the STEM transfer model, noting that the transfer process commences during a student’s community college education and continues through his/her transfer and enrollment in an engineering program at a four-year institution. The following factors contribute to our conceptualization of this process: pre-college background, community college prior to transfer, initial transfer to the four-year university, nearing 4-year degree completion.
Wright College, an open-access community college in northwest Chicago, is an independently accredited institution in the City Colleges of Chicago (CCC) system. Wright is federally recognized Hispanic-Serving Institution (HSI) with the largest enrollment of Hispanic students in Illinois. In 2015 Wright piloted a selective guaranteed admission program to the Grainer College of Engineering at the University of Illinois at Urbana-Champaign (UIUC). Students in the Engineering Pathways (EP) program follow a cohort system with rigorous curriculum aligned to UIUC. From this pilot Wright built programmatic frameworks (one-stop intentional advising; mandatory tutoring, near-peer, faculty and professional mentoring; and access to professional organizations) to support EP students. Initial results were positive: 89% transfer rate and 89% bachelor’s degree completion. Building from the EP frameworks, Wright obtained a National Science Foundation (NSF) HSI research grant to expand programs to non-pathway students. Through the grant, Building Bridges into Engineering and Computer Science, the college developed assessment tools, increased the number of 4-year partnerships, and designed and implemented an Engineering Summer Bridge with curriculum contextualized for the needs of the Near-STEM ready students. These students need one to four semesters of Math remediation before moving into the EP. The college measured the Bridge participants' success through analysis of Math proficiency before and after the Bridge, professional identity (sense of belonging) and self-efficacy (the belief that the students will succeed as engineers). Surveys and case study interviews are being supplemented with retention, persistence, transfer, associate and bachelor degree completion rates, and time for degree completion. The key research question is the correlation of these data with self-efficacy and professional identity measures. Preliminary Results: 1) Sixty percent (60%) of the Bridge participants eliminated the remedial Math requirement completely. (Increased Math proficiency) 2) Engineering admission and enrollment doubled. 4) Increased institutionalized collaborations: the creation of a more programmatic admission, advising, transfer, rigorous curriculum, and other student support services within the College. 5) Increased partnerships with 4-year transfer institutions resulting in the expansion of guaranteed/dual admissions programs with scholarships, paid research experience, dual advising, and students transferring as juniors. 5) Increased diversity in Engineering and Computer Science student population. Wright will share an overview of the Building Bridges into Engineering and Computer Science project, research design, expanded practices, assessments and insights from the development and implementation of this program. The developed frameworks will be applied to provide ALL students at Wright, and at CCC equitable Engineering and Computer Science education.
Aiken, John M., De Bin, Riccardo, Hjorth-Jensen, Morten, and Caballero, Marcos D. Predicting time to graduation at a large enrollment American university. Retrieved from https://par.nsf.gov/biblio/10382952. PLOS ONE 15.11 Web. doi:10.1371/journal.pone.0242334.
Aiken, John M., De Bin, Riccardo, Hjorth-Jensen, Morten, & Caballero, Marcos D. Predicting time to graduation at a large enrollment American university. PLOS ONE, 15 (11). Retrieved from https://par.nsf.gov/biblio/10382952. https://doi.org/10.1371/journal.pone.0242334
@article{osti_10382952,
place = {Country unknown/Code not available},
title = {Predicting time to graduation at a large enrollment American university},
url = {https://par.nsf.gov/biblio/10382952},
DOI = {10.1371/journal.pone.0242334},
abstractNote = {The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto’s Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).},
journal = {PLOS ONE},
volume = {15},
number = {11},
author = {Aiken, John M. and De Bin, Riccardo and Hjorth-Jensen, Morten and Caballero, Marcos D.},
editor = {Vattay, Gábor}
}
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